The role of the media analysis researcher is to study and describe the content and quality of news coverage. Unfortunately media analysis researchers typically use methods that are time consuming, subjective and produce data that is hard to replicate. These limitations often restrict the scope of the research that can be pursued to broad topics over limited spans of time. This means that a variety of topics, in particular health topics that impact populations with health disparities (e.g. Hispanics and African-Americans) are often not studied. Informatics methods like statistical language modeling and probabilistic content modeling can facilitate media analysis by automating certain required tasks. In this research statistical language modeling and probabilistic content modeling will be used to develop automated methods for use in media analysis studies. With these methods researchers will more quickly and efficiently identify any shortcomings in the media's coverage of health topics, in particular topics that impact populations with health disparities. This knowledge can then be made available so that the public can be more informed health news consumers, the media can have the information needed to correct problems that exist in their coverage of health news and the healthcare community can anticipate gaps in the public's perception and knowledge of health issues. The methods used to study and identify shortcomings in the media's coverage of health are typically very manual, subjective and time consuming. In this research automated media analysis methods will be developed and evaluated. If researchers are equipped with improved, more automated methods, then more timely information on the quality of news coverage of health issues, particularly health topics that are important to communities with health disparities can be acquired and shared with the journalists, healthcare professionals and the public.